An Error-Matching Exclusion Method for Accelerating Visual SLAM
This work addresses real-time performance issues in Visual SLAM systems, but it is incremental as it builds on existing GMS-RANSAC methods.
The paper tackles the time-consuming feature matching problem in Visual SLAM by proposing an accelerated method that integrates GMS with RANSAC, achieving a 24.13% reduction in average runtime while maintaining comparable accuracy on standard datasets.
In Visual SLAM, achieving accurate feature matching consumes a significant amount of time, severely impacting the real-time performance of the system. This paper proposes an accelerated method for Visual SLAM by integrating GMS (Grid-based Motion Statistics) with RANSAC (Random Sample Consensus) for the removal of mismatched features. The approach first utilizes the GMS algorithm to estimate the quantity of matched pairs within the neighborhood and ranks the matches based on their confidence. Subsequently, the Random Sample Consensus (RANSAC) algorithm is employed to further eliminate mismatched features. To address the time-consuming issue of randomly selecting all matched pairs, this method transforms it into the problem of prioritizing sample selection from high-confidence matches. This enables the iterative solution of the optimal model. Experimental results demonstrate that the proposed method achieves a comparable accuracy to the original GMS-RANSAC while reducing the average runtime by 24.13% on the KITTI, TUM desk, and TUM doll datasets.